Background Estimation with Gaussian Distribution for Image Segmentation, a fast approach Gianluca Bailo, Massimo Bariani, Paivi Ijas, Marco Raggio IEEE International Workshop on Measurement Systems for Homeland Security, Contraband Detection and Personal Safely

Outline Introduction The method of Stauffer and Grimson Improvement of the method of Stauffer and Grimson Results Conclusion

Introduction Concentrate in the speed of the algorithm with active pixels instead of all the pixels Using Gaussian distributions to model the history of active pixels According to the classification of a part of the background or foreground

Previous Work – background model Simplest  a constant model Problem: illumination, object added to or removed to from the background, shadows, repetitive motion Illumination: use Kalman filtering to update the background model Shadows: only for a particular scene for vehicle detection Must adapt the background !!

Previous Work – background model Repetitive motion & different lightning condition: suffer from slow learning of the background at the beginning and not very fast Lightning, repetitive motion, object added or removed: solve many problems in pixel, region, frame levels, but speed of algorithm is questioned

The method of Stauffer and Grimson Modelling pixel history with K Gaussian probability density distributions The history of a certain pixel is be defined as a time series is a vector in color image scalar in grey level image

The method of Stauffer and Grimson The probability to observe a certain pixel value within the history values of the pixel is is the weight parameter that is used to describe by the Gaussian distribution is a Gaussian distribution that has two parameters: is the mean of the Gaussian distribution at time t and is the covariance matrix at time instant t

The method of Stauffer and Grimson A new pixel is said to match a distribution If it is within 2.5 standard deviations from mean of the distribution This distribution are updated with this pixel value

The method of Stauffer and Grimson is the learning rate that is defined by usesr

The method of Stauffer and Grimson The weight parameters of all distributions are updated as is 1, for matched distribution 0, for unmatched distribution

The method of Stauffer and Grimson If the current pixel didn’t match with any of the K distributions The distribution with smallest weight is replaced by a new distribution The new distribution with The mean is set to the value of the current pixel The variance is set large The weight is set to a small value

The method of Stauffer and Grimson Define which of distributions describing the history of a pixel result from background 1. Order all distributions by a factor 2. B first distributions are marked as background distribution If a pixel is matched with one of these B distributions it is marked as a background pixel

Improvement of the method of Stauffer and Grimson Grey-scale image: not using color images as Stauffer and Grimson did Active/inactive pixels The values of neighboring pixels have a correlation Every other/third pixel is set as active pixel Only active pixels are modelled with K Gaussian distributions

Results Video sequences with 25 frames per second 3 Gaussian distributions were used All tests are done using 1.7 GHz, Pentium 4, 256 MB RAM

Results- method of Stauffer & Grimson

Results-Improvement with every n-th pixel Original methodEvery 2 pixel is examinedEvery third pixel is examined

Result - time The times needed to elaborate one frame

Result – noisy pixels

Conclusions The method of Stauffer and Grimson Illumination – with distribution & update distribution Repetitive motion – with different n distributions New object add to background – with update distribution Improvement method Use every n-th pixel as active pixel to speed surveillance system